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CIPHER: Cybersecurity Intelligent Penetration-Testing Helper for Ethical Researcher.

Derry Pratama¹, Naufal Suryanto², Andro Aprila Adiputra¹

¹School of Computer Science and Engineering, Pusan National University, Busan 46241, Republic of Korea.

Sensors (Basel, Switzerland)

|November 9, 2024

Summary

This summary is machine-generated.

Cybersecurity Intelligent Penetration-testing Helper for Ethical Researchers (CIPHER) is a specialized AI chatbot that assists in penetration testing. CIPHER outperforms larger models, demonstrating the need for domain-specific AI in cybersecurity.

Keywords:

AI penetration testing assistant LLM evaluation domain specific LLM large language model penetration testing pentesting LLM vulnerabillity detection

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Area of Science:

Cybersecurity
Artificial Intelligence
Penetration Testing

Background:

Penetration testing is time-consuming and requires specialized knowledge.
Beginners often need expert guidance for effective vulnerability discovery.
Existing AI models lack domain-specific training for cybersecurity tasks.

Purpose of the Study:

To develop a specialized AI chatbot, CIPHER, for penetration testing assistance.
To create a novel benchmark for evaluating AI in penetration testing.
To address the limitations of general large language models in cybersecurity.

Main Methods:

Trained CIPHER on over 300 penetration testing write-ups and tool documentation.
Introduced the Findings, Action, Reasoning, and Results (FARR) Flow augmentation for automated simulation.
Established a benchmark for evaluating AI technical knowledge and reasoning in pentesting.

Main Results:

CIPHER demonstrated superior performance in providing accurate penetration testing suggestions.
Outperformed similar-sized open-source models and larger state-of-the-art models like Llama 3 70B.
Highlighted the insufficiency of general LLMs for effective penetration testing guidance.

Conclusions:

Specialized AI models like CIPHER are crucial for advancing penetration testing.
The FARR Flow augmentation provides a robust benchmark for AI evaluation.
Further research into scaling and benchmark development is recommended for AI in cybersecurity.